Geometric programming: Difference between revisions

Revision as of 21:14, 10 January 2019

Revision as of 01:14, 15 January 2019

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}}</ref><ref name="tutorial">S. Boyd, S. J. Kim, L. Vandenberghe, and A. Hassibi. ''[http://www.stanford.edu/~boyd/gp_tutorial.html A Tutorial on Geometric Programming].'' Retrieved 8 January 2019.</ref>

Geometric programming is

closely related to [[convex optimization]]: any GP can be made convex by means of a change of variables. <ref name="tutorial"/> GPs have numerous applications, such asincluding component sizing in [[Integrated circuit|IC]] design<ref>M. Hershenson, S. Boyd, and T. Lee. ''[http://www.stanford.edu/~boyd/papers/opamp.html Optimal Design of a CMOS Op-amp via Geometric Programming].'' Retrieved 8 January 2019.</ref><ref> S. Boyd, S. J. Kim, D. Patil, and M. Horowitz. ''[http://www.stanford.edu/~boyd/gp_digital_ckt.html Digital Circuit Optimization via Geometric Programming].'' Retrieved 8 January 2019.</ref>, aircraft design<ref>W. Hoburg and P. Abbeel. ''[https://people.eecs.berkeley.edu/~pabbeel/papers/2014-AIAA-GP-aircraft-design.pdf Geometric programming for aircraft design optimization].'' AIAA Journal 52.11 (2014): 2414-2426.</ref>, and [[maximum likelihood estimation]] for [[logistic regression]] in [[statistics]].

==Convex form==

Geometric programs are not in general convex optimization problems, but they can be transformed to convex problems by a change of variables and a transformation of the objective and constraint functions. In particular, after performing the change of variables <math>y_i = \log(x_i)</math> and taking the log of the objective and constraint functions, the functions <math>f_i</math>, i.e., the posynomials, are transformed into [[LogSumExp | log-sum-exp]] functions, which are convex, and the functions <math>g_i</math>, i.e., the monomials, become [[affine transformation | affine]]. Hence, this transformation transforms every GP into an equivalent convex program. <ref name="tutorial"/> In fact, this log-log transformation can be used to convert a larger class of problems, known as log-log convex programsprogramming (LLCP), into an equivalent convex form. <ref name="dgp">A. Agrawal, S. Diamond, and S. Boyd. ''[https://arxiv.org/abs/1812.04074 Disciplined Geometric Programming.]'' Retrieved 8 January 2019.</ref>

==Software==

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{{reflist}}

[[Category:OptimizationConvex algorithms and methodsoptimization]]